The 2- 1 compressed sensing minimization problem can be solved efficiently by gradient projection. In imaging applications, the signal of interest corresponds to nonnegative pixel...
Zachary T. Harmany, Daniel Thompson, Rebecca Wille...
The theory of compressed sensing tells a dramatic story that sparse signals can be reconstructed near-perfectly from a small number of random measurements. However, recent work ha...
This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal o...
A method is proposed for the compression of hyperspectral signature vectors on severely resourceconstrained encoding platforms. The proposed technique, compressive-projection prin...
In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study is carried out by evaluating the compressed features in ...